Why is my Keras prediction always close to 100% for one image class?
Traditional neural networks can be over-confident (i.e. give a probability close to $0$ or $1$) even when they are wrong, so you should not interpret the probability that it produces as a measure of uncertainty (i.e. as a measure of how much it is confident that the associated predicted class is the correct one), as that it is essentially wrong. See this and this answers for more details about this.
Given that this overconfidence is not desirable in many scenarios (such as healthcare, where doctors also want to know how confident the model is about its predictions, in order to decide whether to give a certain medication to the patient or not), the ML community has been trying to incorporate uncertainty quantification/estimation in neural networks. If you are interested in this topic, you could read the paper Weight Uncertainty in Neural Network (2015) by Blundell et al., which proposes a specific type of Bayesian neural network, i.e. a neural network that models the uncertainty over the actual values of the weights, from which we may also quantify/estimate the uncertainty about the inputs. This paper should not be too difficult to read if you are already familiar with the details of variational-autoencoders.
So, the answer to your question is: yes, it's possible that the output probability is close to $1$ because neural networks can be over-confident. (I am assuming that the values returned by tf.keras's predict method are probabilities: I don't remember anymore, so I assumed that you did not make any mistake).
A similar question was already asked in the past here. The accepted answer should provide more details about different types of uncertainty and solutions.